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command: |
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- python3 |
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- ${program} |
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- --do_train |
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- --do_eval |
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- --use_scan |
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- --gradient_checkpointing |
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- --overwrite_output_dir |
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- --predict_with_generate |
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- --freeze_encoder |
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- --streaming |
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- --use_auth_token |
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- --compilation_cache |
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- --return_timestamps |
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- ${args} |
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method: grid |
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metric: |
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goal: minimize |
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name: eval/wer |
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parameters: |
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model_name_or_path: |
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value: distil-whisper/large-32-2 |
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teacher_model_name_or_path: |
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value: openai/whisper-large-v2 |
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train_dataset_name: |
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value: librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+spgispeech-timestamped |
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train_dataset_config_name: |
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value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+L |
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train_split_name: |
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value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train |
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train_dataset_samples: |
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value: 2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+192.7 |
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timestamp_probability: |
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values: |
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- 0.0 |
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- 0.2 |
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- 0.4 |
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- 0.6 |
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- 0.8 |
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- 1.0 |
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round_timestamps: |
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values: |
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- True |
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- False |
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eval_dataset_name: |
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value: "distil-whisper/gigaspeech-l" |
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eval_dataset_config_name: |
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value: "l" |
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cache_dir: |
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value: /home/sanchitgandhi/.cache |
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dataset_cache_dir: |
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value: /home/sanchitgandhi/.cache |
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output_dir: |
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value: ./ |
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per_device_train_batch_size: |
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value: 64 |
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dtype: |
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value: bfloat16 |
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learning_rate: |
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value: 1e-4 |
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lr_scheduler_type: |
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value: constant_with_warmup |
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warmup_steps: |
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value: 50 |
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max_steps: |
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value: 2500 |
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save_steps: |
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value: 2501 |
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dataloader_num_workers: |
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value: 48 |
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logging_steps: |
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value: 25 |
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wer_threshold: |
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value: 10 |
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program: run_distillation.py |
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project: distil-whisper-sweeps |
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